Feature selection with nonparametric statistics

In this paper we discuss a general framework for feature selection based on nonparametric statistics. The three stage approach we propose is based on the assumption that the available data set is representative of a certain concept and aims at learning from the data the selection of a subset of descriptive features out of a large pool of measurements. The first stage requires the computation of a large number of image features. Simple significance tests and the maximum likelihood principle are at the basis of the second stage in which a saliency measure is used to reject the features which do not appear to be descriptive of the given data set. The third and final stage, by using the Spearman independence rank test, selects a maximal number of pairwise independent features. We report experiments on a face dataset (the MIT-CBCL database) which confirm the quality and the potential of the approach.